Anomaly detection commonly refers to detecting objects with behavior that deviates (perhaps significantly) from expected behavior. Within the context of electrical networks, anomaly detection can include, for example, detecting theft which is intentionally caused by one or more consumers in the electrical network, or detecting any other abnormal behavior which may be caused by mechanical damage in the network.
Additionally, within the context of electrical networks, non-technical losses can cause an unexpected consumption of electricity, significant loss for utilities, and/or a rise in electricity price which can create a burden for consumers. Non-technical losses in an electricity distribution network, as used herein, can include electricity theft as well as losses due to malfunctioning of electrical equipment, poor maintenance, and/or other unexpected behavior causing abnormal power consumption and waste. Existing detection approaches face challenges in detecting non-technical losses due, for example, to the large size of distribution networks in terms of the number of consumers and the total physical span of the networks. Additional challenges are presented due, for example, to the different methods that can be used in electricity theft such as tampering, bypassing the meters, hooking from the line etc., which can be difficult to detect other than by manual inspection by a human expert.